Recently, several approaches to learning a controller have been presented. Pfeiffer et al.propposed to train an end-to-end deep network motion planner for small, static maps using the raw sensor data as input, the motor controls as output, and a ROS path planner as the expert teacher [1]}. Chen, Everett et al.[2]}, [3]} trained a deep network to navigate among moving obstacles. In order to process an undetermined number of obstacles, an LSTM layer was used to convert an arbitrary number of observed agents to a fixed size input for the network. The obstacles were represented as circular objects encoded by their position, velocity, and radius. This setup is not well suited to encode the large, nonconvex structures of indoor environments. Kretzschmar et al.[4]} used inverse reinforcement learning to identify the parameters of human trajectories that were modeled by cubic splines to achieve a predictive system with human-like behavior.
